Chikungunya virus (CHIKV) is mosquito-borne alphavirus that causes explosive outbreaks of acute and persistent arthritis. Since its re-emergence in 2004, CHIKV has caused millions of cases of debilitating arthritis in countries within and around the Indian Ocean, and the recent introduction of CHIKV into the Americas has led to an ongoing large scale epidemic within the Caribbean, and South and Central America. This outbreak has also led to the introduction and localized spread of CHIKV in Florida, demonstrating that the current CHIKV epidemic may also be a threat to the United States. The factors that contribute to alphavirus-induced arthritis are incompletely understood, however, a large body of evidence suggests that the host inflammatory response plays a major role in driving disease pathogenesis. Importantly, many of the components of the inflammatory response that are associated with CHIKV disease are genetically polymorphic, and there is also significant variation in disease severity in persons suffering from CHIKV-induced disease. This suggests that host genetic variation may impact susceptibility to CHIKV-induced disease, which is supported by the fact that different mouse strains exhibit significant variation in susceptibiliy to CHIKV-induced arthritis. In preliminary studies using the Collaborative Cross (CC), a mouse genetic reference population designed to model the genetic diversity found in humans, we find significant variation in susceptibility to CHIVK-induced disease between CC lines, with some lines being resistant, while others are highly susceptibility to CHIKV-induced arthritis. This indicates that host genetic variation strongly impacts CHIKV disease susceptibility and raises the possibility that the CC can be used to identify polymorphic genes that impact CHIKV disease. Therefore, we propose to use the CC to identify and study polymorphic genes and gene expression networks that impact CHIKV-disease susceptibility. By combining our extensive experience in CHIKV pathogenesis with our expertise in the use of state of the art quantitative genetics and bioinformatics methodologies, we will gain new insight into the genes and host signaling networks that regulate CHIKV disease outcomes, while also identifying potential host targets for treating CHIKV-induced arthritic disease.